SIMULATION

This article focuses on the planning and scheduling of operating rooms (ORs) in a regional hospital in Sweden. A simulation study was carried out to find new ideas and new planning and scheduling techniques to improve the overall process of surgery, including pre- and post-operating activities. This study mainly addresses the problem of low utilization of the ORs, and also takes into consideration problems with variation in workload, both in ORs and in post-anesthesia care units. The final simulation model includes pre-operative care carried out in the operating department and all ORs, as well as post-operative care units. It was driven by a number of input parameters, such as the volume and specific characteristics of actual cases, opening hours and number of ORs, and the number of beds for pre- and post-operative care. The model also includes logic for prioritizing and allocating cases to available ORs, planning operating schedules and the utilization of medical equipment limited in quantity. Output performance measures from simulation experiments include the utilization of allocated OR times, waiting time for patients, queue dynamics, number of cancellations, and variation of finishing times, as well as occupancy statistics in the post-operative care unit. Four different change alternatives were evaluated using the simulation model. Simulation experiments showed that with the implementation of the proposed changes it is possible to achieve slightly better and more even resource utilization, as well as provide greater flexibility in scheduling operations.

Increased demand for specialized healthcare services has been identified as one of the causes of increased healthcare costs in the US. Nuclear medicine, a sub-specialty of radiology, uses relatively new technology to diagnose and treat patients. Procedures (tests) in nuclear medicine require the use of radiopharmaceuticals with a limited half-life and involve several steps that are constrained by strict time windows and require multiple resources for completion. Consequently, managing patient service in nuclear medicine is a very challenging problem that has received very little research attention. In this paper, we present a discrete event system specification (DEVS) simulation model for nuclear medicine patient service management that considers both patient and management perspectives. DEVS is a formal modeling and simulation framework based on dynamical systems theory and provides well-defined concepts for coupling components, hierarchical and modular model construction, and an object-oriented substrate supporting repository reuse. We report on simulation results based on historical data using both patient and management performance measures. The results provide useful insights regarding the management of patient service in nuclear medicine. While this work focuses on nuclear medicine, results will find generality in other healthcare settings.

Open access (OA) is a scheduling system which leaves the majority of the slots open to same-day appointments (SDAs). The OA is expected to reduce patient waiting time and no-show rate, and, in turn, increase clinic performance including patient satisfaction. Although many success stories have been reported, there is no study investigating the impact of OA configuration considering environmental conditions. In this paper, we conducted a simulation study in an outpatient clinic setting. The clinical environments we consider include the demand variability, no-show rate, and the ratio of SDA patients. The OA configurations are constructed by the slots for pre-book which is a complement of SDA and the scheduling horizon for the pre-book. The experimental results demonstrate the performance of different OA configurations under various clinical environments in terms of patient waiting time, patient rejection rate and clinic utilization. The results are scrutinized in the method of a multi-objective optimization.

Today’s workflow management systems (WfMSs) offer workitems to users through specific work-lists. Users select the workitems they will perform without having a schedule in mind. However, in many environments work needs to be scheduled and performed at particular times. For example, in hospitals many workitems are linked to appointments, e.g., a doctor cannot perform surgery without reserving an operating theater and making sure that the patient is present and ready. One of the problems when applying workflow technology in such domains is the lack of calendar-based scheduling support. In collaboration with the Academic Medical Center (AMC), a large hospital in the Netherlands, we developed a schedule-aware WfMS that supports the seamless integration of unscheduled (flow) and scheduled (schedule) tasks. However, before deployment of the resultant system in the hospital, a seamless integration with AMC’s running healthcare processes needs to be guaranteed. Therefore, for a large and complex healthcare process, we apply computer simulation to validate and to investigate, for different configurations of the system, the operational performance for a selected healthcare process when supported by the schedule-aware workflow management system. One of the important characteristics of our approach is the tight coupling between the simulation model and the actual implemented system. While performing simulation experiments, parts of the system may be simulated using CPN Tools while connected to the actual system components. Our simulation experiments demonstrate that the developed schedule-aware WfMS can be safely applied in the AMC hospital.

The publications that relate to the application of simulation to healthcare have steadily increased over the years. These publications are scattered amongst various journals that belong to several subject categories, including operational research, health economics and pharmacokinetics. The simulation techniques that are applied to the study of healthcare problems are also various. The aim of this study, therefore, is to review healthcare simulation literature that have been published between 1970 and 2007 in high-quality journals belonging to various subject categories and that report on the application of four simulation techniques, namely, Monte Carlo simulation, discrete-event simulation, system dynamics and agent-based simulation. Arguably, journal impact factor is fundamental in assessing the quality of publications. Thus, the 201 publications selected for review have been queried from the ISI Web of Science® bibliographic database of high-impact research journals. Through a review of healthcare simulation literature the following three objectives have been realized: (a) papers have been categorized under the different simulation techniques, and the healthcare problems that each technique is employed to investigate are identified; (b) variables such as authors, article citations, etc., within our dataset of healthcare papers have been profiled; (c) turning point (strategically important) papers and authors have been identified through co-citation analysis of references cited by the papers in our dataset. The above objectives have been realized by devising and then employing a methodology for profiling literature. It is expected that this review paper will help the readers gain a broader understanding of research in healthcare simulation.

The problem of emergency department (ED) overcrowding has reached crisis proportions in the last decade. In 2005, the National Academy of Engineering and the Institute of Medicine reported on the important role of simulation as a systems analysis tool that can have an impact on care processes at the care-team, organizational, and environmental levels. Simulation has been widely used to understand causes of ED overcrowding and to test interventions to alleviate its effects. In this paper, we present a systematic review of ED simulation literature from 1970 to 2006 from healthcare, systems engineering, operations research and computer science publication venues. The goals of this review are to highlight the contributions of these simulation studies to our understanding of ED overcrowding and to discuss how simulation can be better used as a tool to address this problem. We found that simulation studies provide important insights into ED overcrowding but they also had major limitations that must be addressed.

This paper addresses the problem of maximizing the utilization of operating rooms, which is translated to jobs scheduling in an identical parallel machine environment with sequence-dependent setup times and an objective of minimizing the makespan. The jobs’ processing times and setup times are stochastic for better depiction of the real world. This is a non-deterministic polynomial time (NP)-hard problem, and in this paper a new heuristic is developed and compared to existing ones using simulation and optimization. The results and analysis obtained from the computational experiments proved the superiority of the proposed algorithm Longest Expected Processing with Setup Time (LEPST) over the other algorithms presented.